The resolution of SAR data is not comparable to the resolution of electro-optical (EO) data. EO sensors include photographic and other optical imaging devices, such as light detection and ranging (LIDAR) collectors. EO sensors are passive in that they capture the reflectivity of light from scenes to provide photographic images thereof. However, EO sensors are limited by time-of-day and atmospheric conditions.
A synthetic aperture radar (SAR) is advantageous in that images can be acquired day or night, as well as in inclement weather. A SAR is active in that it records back-scattered radiation from radio frequency (RF) signals to generate SAR images. Each resolution cell of the SAR generally has many scatterers. The phases of the return signals from these scatterers are randomly distributed, and the resulting interference causes speckle.
Speckle gives a grainy appearance in the detected image that is finally viewed, and hence a lower resolution when compared to an EO image. Speckle imposes a significant limitation on the accuracy of the measurements that can be made. For instance, mensuration is often inclusive in SAR data. Side-lobe interference also creates a noisy look to the SAR data. In addition, hardware malfunctions or radio interference can decrease the fidelity of the SAR data.
SAR data is currently being treated with some form of apodization in which the main and side lobes are removed. However, apodization makes SAR data look binary. This also results in the detected image having a grainy appearance. SAR data is also being treated with low pass filters, such as Taylor weighting. However, the scatterers can become blurred together resulting in a reduced resolution. As a result of the current approaches used to treat SAR data, certain analysis applications can be inclusive, including registration, road detection, change detection, elevation extraction and mensuration.
For SAR images that contain speckle, an enhancement goal is to remove the speckle without destroying important image features. The brightness of a pixel is determined not only by properties of the scatterers in the resolution cell, but also by the phase relationships between the returns from these scatterers. In certain applications, however, the removal of speckle may be counterproductive. An example in which speckle preservation is important is where detection of features is of the same scale as the speckle patterns. A known technique for despeckling SAR data as well as resolution enhancement is the application of anisotropic diffusion algorithms.
One approach for despeckling SAR data is disclosed in the article titled “Speckle Reducing Anisotropic Diffusion” by Yu et al. A partial differential equation (PDE) approach is used for speckle removal. In particular, an image scale space is generated, which is a set of filtered images that vary from fine to coarse. Another approach is disclosed in the article titled “Anisotropic Diffusion Despeckling For High Resolution SAR Images” by Xi et al. A non-linear diffusion filtering algorithm based on a discretization scheme, i.e., an additive operator splitting (AOS) scheme, is applied in the discrete image data. While both of these approaches result in improving the resolution of the SAR data by reducing noise and preserving edges, there is still a demand to make SAR data look more like high resolution EO data.